Here we report several key findings relating brain structural deficits to obesity, higher BMI, FPI and DM2 in cognitively normal elderly individuals drawn from a community cohort. First, higher body tissue fat was strongly associated with brain volume deficits in cognitively normal elderly subjects, even when controlling for potential confounds such as age, sex, and race. Second, FPI and DM2 showed inverse associations with brain structure in a bivariate analysis, but these correlations were not statistically significant when controlling for BMI. Third, negative correlations between body tissue fat and brain structure were strongest in obese persons but were also seen in overweight individuals. While we acknowledge that the effects of obesity may be secondary to generally poor health, this is less likely in our sample because (i) those with very poor health are less likely to survive to the advanced age (mean: 77.3 years) in our study; (ii) no correlation was detected between BMI and death rates in our cohort 10 years after their scan (r(94) = 0.07, p = 0.47); and (iii) the 3 BMI groups did not differ in their rates of vascular diseases that increase morbidity and mortality (). Therefore, even in persons with normal cognition who survive to old age, higher body tissue adiposity may have deleterious consequences on brain structure.
Our finding of BMI-associated brain atrophy in cognitively normal elderly is supported by studies from younger samples. A study of Japanese males (mean age: 46.1) showed reduced GM volumes in association with increasing BMI in medial temporal lobes, hippocampus, and precuneus [Taki et al., 2008
]. Another study (mean age: 32) showed greater GM volume loss in obese individuals in the frontal operculum, postcentral gyrus, and putamen [Pannacciulli et al., 2006
]. A recent MR spectroscopy study revealed metabolic abnormalities in frontal lobe GM and WM in a group of younger obese persons (mean age: 41.7) [Gazdzinski et al., 2008
The correlation between BMI and brain volumes is unlikely to be direct in the sense of one causing the other; therefore, it is of interest to identify factors or mechanisms that
might tend to cause brain volume reduction and obesity in the same subjects. The most commonly proposed mediators for the relationship between higher body tissue adiposity and brain structure include hypercortisolemia [Lupien et al., 1998
], reduced exercise [Colcombe et al., 2003
], impaired respiratory function [Guo et al., 2006
], inflammation [van Dijk et al., 2005
], cardiovascular/hypertension/hyperlipidemia [Breteler et al., 1994
; Swan et al., 1998
], and type II diabetes mellitus [den Heijer et al., 2003
; Ferguson et al., 2003
]. The manifestations of brain structural deficits in these studies were hippocampal atrophy, cortical volume loss, and WM hyperintensities. We found no interaction between BMI and DM2, so the effects of BMI are unlikely to be mediated by that mechanism in our sample. Additionally, our BMI results did not change when controlling for hypertension and WM hyperintensities as assessed by standardized CHS criteria [Dai et al., 2008
; Yue et al., 1997
]. These results may reflect a survivor effect, as persons with both high BMI and clinically severe cerebrovascular disease are less likely to live to the age range of our study population (70–89 years). Additionally, we cannot rule out the possibility that BMI relationships with brain atrophy in our elderly cohort are more directly mediated through any one or any combination of the othermechanisms listed above.
Having established that BMI is associated with brain atrophy in the elderly, we also acknowledge that controversy exists in the literature about how this association is influenced by sex differences. A group of elderly (70–84 years) Swedish women showed substantial temporal lobe atrophy on computed tomography [Gustafson et al., 2004
] while another study found BMI associated cerebral volume loss in Japanese men but not in women [Taki et al., 2008
]. To determine whether or not correlations between BMI and brain structure are influenced by gender in our study, we modeled a BMI by gender interaction in our multiple regression
analyses and did not detect a sex difference in BMI-related brain atrophy. Our study therefore suggests that the deleterious effects of higher tissue adiposity on brain structure may be gender independent; however, this finding merits further investigation in future studies.
Even though the unadjusted correlations of FPI, DM2, and brain atrophy were not statistically significant in the adjusted models, they may merit discussion due to a growing literature on the effects of hyperinsulinemia and DM2 on the brain. In the early stages of DM2, insulin resistance is associated with a compensatory hyperinsulinemia [Yaffe et al., 2004
], and high insulin levels are associated with cognitive impairment, even in subjects who will not develop DM2 [van Oijen et al., 2008
], suggesting that hyperinsulinemia can alter brain structure. Multiple mechanisms are involved in the impact of hyperinsulinemia on brain function and structure, including vasoactive effects on cerebral arteries, neurotoxicity due to impaired clearance of amyloid from the brain and stimulation of the formation of neurofibrillary tangles through advanced glycation end-product metabolism [Bian et al., 2004
; Watson et al., 2003
]. The insulin effect is observed here in multiple areas relevant to cognitive function such as the orbital frontal cortex and the hippocampus. This is consistent with the notion that hyperinsulinemia affects brain structures involved in cognition; it may also lead to subtle cognitive decline before clear clinical symptoms of dementia are detectable [Kalmijn et al., 1995
DM2 was associated with lower GM and WM volumes areas of cognitive relevance such as the frontal lobes and large WM tracts (splenium of the corpus callosum), suggesting that DM2 has a widespread association with brain atrophy. DM2 can reduce brain volume through a progressive cerebrovascular process that leads to stroke and infarcts [Ikram et al., 2008
; Knopman et al., 2005
]. DM2 can exert damage through advanced glycation of key structural proteins, imbalance between production and elimination of reactive oxygen species, and through perturbations of hexosamine and polyol pathways, causing the basement membranes of cerebral capillaries to thicken [Arvanitakis et al., 2006
]. Such microvascular changes, which frequently occur with other obesity consequences such as hypertension, can lead to chronic subclinical ischemia, impaired neuronal energy consumption, and atrophy in brain areas with delicately vulnerable vasculature such as the lenticulostriate arteries of the basal ganglia [Breteler et al., 1994
]. Basal ganglial findings in TBM analyses can also be noticeable due to a comparative lack of sensitivity TBM has to volume changes in the cortical surface due to smoothness of the deformation fields and resulting partial volume effects [Hua et al, 2009; Leow et al., 2009
]. Our bivariate
DM2 results are consistent with prior findings that GM and WM are affected in DM2 [Korf et al., 2007
; Tiehuis et al., 2008
] and with FDG-PET studies that showed hypometabolism in frontal, temporal, and parietal association regions, and posterior cingulate gyrus in cognitively normal subjects with mild hyperglycemia [Kawasaki et al., 2008
The DM2 association did not survive the adjusted multiple regression models, which may be due to the small number of DM2 subjects in the study (n = 11), that itself may be a consequence of a survivor effect. That is, many persons with DM2 may not have lived long enough to undergo scanning as part of the CHS. This bias may have led to lack of power in the multiple regression models and lack of a statistically significant interaction between BMI and DM2. This issue could be overcome in future studies by analyzing larger numbers of cognitively normal elderly DM2 persons. Such work could elucidate a possible mediation role for DM2 with respect to obesity and brain atrophy.While it is tempting to speculate that obese and overweight persons harbor early subclinical DM2 pathology (as reflected by obese and overweight persons having higher FPI) and that this drives the relationship between BMI and brain atrophy, future work would have to verify this as we found no statistically significant interactions between BMI and DM2 or FPI.
Our findings, taken in the context of earlier studies, suggest that elderly persons with higher adiposity are at increased risk for brain atrophy and consequently dementia. Even our elderly subjects, who were very healthy and confirmed to be cognitively stable for at least 5 years after baseline scanning, were afflicted with brain atrophy associated with obesity. Our results suggest that individuals may have a greater extent of brain atrophy due to obesity or due to factors that promote obesity and that this atrophy may, in turn, predispose them to future cognitive impairment and dementia. The implications of this cycle include: (i) amplified morbidity/mortality in the elderly; (ii) higher health care costs due to obesity-related dementia; and (iii) emotional and other non-financial burdens on caretakers and healthcare providers. Obesity associations with brain atrophy and dementia risk therefore present a potential public health challenge.
This study used neuroimaging methods to explore the effects of higher BMI, insulin, and DM2 in an elderly community cohort who remained cognitively normal for five years after their scan. Such results are therefore more likely to reflect brain changes in the general elderly population as they avoid the referral biases of studies that draw subjects from specialty clinics. Tensor-Based Morphometry (TBM) offers high resolution mapping of anatomical differences, offering excellent sensitivity to systematic structural differences in the brain, and lacks the selection bias of ROI tracings that examine only part of the brain. We used TBM because of its effectiveness in analyzing volumetric group differences in the entire brain. In other types of voxel-based studies, such as voxel-based morphometry [Ashburner and Friston, 2000
], a question sometimes arises as to whether the findings may be attributable to imperfect registration. This question arises because in VBM, smoothed maps of classified gray matter are automatically aligned across subjects and smoothed, and then statistical inferences are made regarding group differences, by voxel-by-voxel subtraction of the group-averaged images. As such it is possible that a difference detected at any one location is due to imperfect registration [Thacker et al., 2004
In TBM, however, the signals analyzed are based only on the registrations of the images and not the aligned gray matter classifications, so it is not required that the gray matter be perfectly registered across subjects as the gray matter density is not analyzed at each stereotactic location. As such, false positive findings due to systematic group differences in registration errors are less likely. Even so, there may be false negative findings, because the power to detect morphometric differences depends on the scale at which anatomic data can be matched by the warping algorithm. Finer-scale morphometric differences (e.g. in the hippocampus or cortical thickness) may be better detected using other methods that model those structures explicitly. However, we preferred use of TBM over cortical pattern matching as TBM is able to process larger numbers of subjects in faster times and requires less computational memory [Xue et al., 2008]. TBM is therefore less vulnerable to registration bias than VBM and more efficient for analyzing larger numbers of subjects than cortical surface modeling and cortical pattern matching.
Our findings are limited by the cross-sectional design, though longitudinal follow-up was used to inform subject selection to minimize confounding from those experiencing early neurodegeneration from Alzheimer's or other dementias. Our multiple regression approach accounted for the potentially confounding effects of age, gender, and race and DM2. We did not include APOE4 genotype in this model, as the variable showed no statistically significant relationships in the bivariate analysis (p = 0.39, permutation test).
With an increasing number of persons becoming both obese and elderly, a detailed understanding of brain structural abnormalities in this group is vital. Studies such as this suggest why these individuals may have an increased risk for dementia. Even elderly individuals who remained cognitively normal long after their MRI had BMI associated atrophy in brain areas targeted by neurodegeneration: hippocampus, frontal lobes, and thalamus. Such individuals may benefit from interventions to reduce body tissue fat and experience better brain health in aging.